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Beyond ab initio modelling… Comparative and Boltzmann equilibrium Yann Ponty, CNRS/Ecole Polytechnique with invaluable help from Alain Denise, LRI/IGM,

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Presentation on theme: "Beyond ab initio modelling… Comparative and Boltzmann equilibrium Yann Ponty, CNRS/Ecole Polytechnique with invaluable help from Alain Denise, LRI/IGM,"— Presentation transcript:

1 Beyond ab initio modelling… Comparative and Boltzmann equilibrium Yann Ponty, CNRS/Ecole Polytechnique with invaluable help from Alain Denise, LRI/IGM, Université Paris-Sud M2 Bioinfo Paris-Saclay 2015-2016 1

2 Prediction by homology M2 Bioinfo Paris-Saclay 2015-20162  Data : several homologous RNA sequences.  Output : a consensus structure for this set of sequences.

3 Prediction by Homology From sequence alignment 3M2 Bioinfo Paris-Saclay 2015-2016

4 Detecting covariations M2 Bioinfo Paris-Saclay 2015-20164  We start from a sequence alignment: GAGGACTGAGCTCAGTTAAAGTGCCTG AAGGGCCCCGCTGGGCAAAG--GCTG- AAGGGGTCGGCTGACCTAAAGTAGTTG GAGGGGTGAG-GCAUCTAAAGTGTTTG GAGGACTGTGCTCAGTTAAAGTGTTTG  Look for sequence covariations

5 Detecting covariations M2 Bioinfo Paris-Saclay 2015-20165  We start from a sequence alignment: GAGGACTGAGCTCAGTTAAAGTGCCTG AAGGGCCCCGCTGGGCAAAG--GCTG AAGGGGTCGGCTGACCTAAAGTAGTTG GAGGGGTGAG-GCAUCTAAAGTGTTTG GAGGACTGTGCTCAGTTAAAGTGTTTG ( )  We search for sequence covariations,  They come from compensatory mutations during the evolution

6 Detecting covariations M2 Bioinfo Paris-Saclay 2015-20166  We start from a sequence alignment: GAGGACTGAGCTCAGTTAAAGTGCCTG AAGGGCCCCGCTGGGCAAAG--GCTG AAGGGGTCGGCTGACCTAAAGTAGTTG GAGGGGTGAG-GCAUCTAAAGTGTTTG GAGGACTGTGCTCAGTTAAAGTGTTTG....((((....))))...........  We search for sequence covariations  They come from compensatory mutations during the evolution

7 Detecting covariations M2 Bioinfo Paris-Saclay 2015-20167  We start from a sequence alignment: GAGGACTGAGCTCAGTTAAAGTGCCTG AAGGGCCCCGCTGGGCAAAG--GCTG AAGGGGTCGGCTGACCTAAAGTAGTTG GAGGGGTGAG-GCAUCTAAAGTGTTTG GAGGACTGTGCTCAGTTAAAGTGTTTG....((((....))))...........  Measure : mutual information between positions i and j : - ∑ Pr(i=a) Pr(j=b) log(Pr(i=a|j=b)) a,b where a and b are the different nucleotides.

8 Two softwares based on this approach M2 Bioinfo Paris-Saclay 2015-20168  RNA-alifold (Hofacker et al. 2000) http://rna.tbi.univie.ac.at/cgi-bin/RNAalifold.cgi  RNAz (Washietl et al. 2005) http://rna.tbi.univie.ac.at/cgi-bin/RNAz.cgi

9 RNAalifold 9M2 Bioinfo Paris-Saclay 2015-2016

10 Application : tRNA Alanine >Artibeus_jamaicensis AAGGGCTTAGCTTAATTAAAGTAGTTGATTTGCATTCAGCAGCTGTAGGATAAAGTCTTGCAGTCCTTA >Balaenoptera_musculus GAGGATTTAGCTTAATTAAAGTGTTTGATTTGCATTCAATTGATGTAAGATATAGTCTTGCAGTCCTTA >Bos_taurus GAGGATTTAGCTTAATTAAAGTGGTTGATTTGCATTCAATTGATGTAAGGTGTAGTCTTGCAATCCTTA >Canis_familiaris GAGGGCTTAGCTTAATTAAAGTGTTTGATTTGCATTCAATTGATGTAAGATAGATTCTTGCAGCCCTTA >Ceratotherium_simum GAGGGTTTAGCTTAATTAAAGTGTTTGATTTGCATTCAGTTGATGTAAGATAGAGTCTTGCAGCCCTTA >Dasypus_novemcinctus GAGGACTTAGCTTAATTAAAGTGCCTGATTTGCGTTCAGGAGATGTGGGGCTAAATCTTGCAGTCCTTA >Equus_asinus AAGGGCTTAGCTTAATGAAAGTGTTTGATTTGCGTTCAATTGATGTGAGATAGAGTCTTGCAGTCCTTA >Erinaceus_europeus GAGGATTTAGCTTAAAAAAAGTGGTTGATTTGCATTCAATTGATATAGGAAATATAATCTTGTAATCCTTA >Felis_catus GAGGACTTAGCTTAATTAAAGTGTTTGATTTGCAATCAATTGATGTAAGATAGATTCTTGCAGTCCTTA >Hippopotamus_amphibius AGGGACTTAGCTTAATAAAAGCAGTTGAGTTGCATTCAATTGATGTGAGGTGCGGTCTTGCAGTCTCTA >Homo_sapiens AAGGGCTTAGCTTAATTAAAGTGGCTGATTTGCGTTCAGTTGATGCAGAGTGGGGTTTTGCAGTCCTTA 10M2 Bioinfo Paris-Saclay 2015-2016

11 Exercise M2 Bioinfo Paris-Saclay 2015-201611 1. Compute an alignment of the previous sequences, by using MAFFT: http://www.ebi.ac.uk/Tools/msa/mafft/ (do not forget to set the Nucleic Acid option) http://www.ebi.ac.uk/Tools/msa/mafft/ 2. Copy/paste the result in RNAalifold : http://rna.tbi.univie.ac.at/cgi-bin/RNAalifold.cgihttp://rna.tbi.univie.ac.at/cgi-bin/RNAalifold.cgi 3. Look at the result.

12 MAFFT alignment >Artibeus_jamaicensis AAGGGCTTAGCTTAATTAAAGTAGTTGATTTGCATTCAGCAGCTGTAGG--ATAAAGTCTTGCAGTCCTTA >Balaenoptera_musculus GAGGATTTAGCTTAATTAAAGTGTTTGATTTGCATTCAATTGATGTAAG--ATATAGTCTTGCAGTCCTTA >Bos_taurus GAGGATTTAGCTTAATTAAAGTGGTTGATTTGCATTCAATTGATGTAAG--GTGTAGTCTTGCAATCCTTA >Canis_familiaris GAGGGCTTAGCTTAATTAAAGTGTTTGATTTGCATTCAATTGATGTAAG--ATAGATTCTTGCAGCCCTTA >Ceratotherium_simum GAGGGTTTAGCTTAATTAAAGTGTTTGATTTGCATTCAGTTGATGTAAG--ATAGAGTCTTGCAGCCCTTA >Felis_catus GAGGACTTAGCTTAATTAAAGTGTTTGATTTGCAATCAATTGATGTAAG--ATAGATTCTTGCAGTCCTTA >Equus_asinus AAGGGCTTAGCTTAATGAAAGTGTTTGATTTGCGTTCAATTGATGTGAG--ATAGAGTCTTGCAGTCCTTA >Homo_sapiens AAGGGCTTAGCTTAATTAAAGTGGCTGATTTGCGTTCAGTTGATGCAGA--GTGGGGTTTTGCAGTCCTTA >Hippopotamus_amphibius AGGGACTTAGCTTAATAAAAGCAGTTGAGTTGCATTCAATTGATGTGAG--GTGCGGTCTTGCAGTCTCTA >Dasypus_novemcinctus GAGGACTTAGCTTAATTAAAGTGCCTGATTTGCGTTCAGGAGATGTGGG--GCTAAATCTTGCAGTCCTTA >Erinaceus_europeus GAGGATTTAGCTTAAAAAAAGTGGTTGATTTGCATTCAATTGATATAGGAAATATAATCTTGTAATCCTTA 12M2 Bioinfo Paris-Saclay 2015-2016

13 RNAalifold 13M2 Bioinfo Paris-Saclay 2015-2016

14 Application : tRNA H.sapiens >Homo_sapiensArg TGGTATATAGTTTAAACAAAACGAATGATTTCGACTCATTAAATTATGATAATCATATTTACCAA >Homo_sapiensAsn TAGATTGAAGCCAGTTGATTAGGGTGCTTAGCTGTTAACTAAGTGTTTGTGGGTTTAAGTCCCATTGGTCTAG >Homo_sapiensAsp AAGGTATTAGAAAAACCATTTCATAACTTTGTCAAAGTTAAATTATAGGCTAAATCCTATATATCTTA >Homo_sapiensCys AGCTCCGAGGTGATTTTCATATTGAATTGCAAATTCGAAGAAGCAGCTTCAAACCTGCCGGGGCTT >Homo_sapiensGln TAGGATGGGGTGTGATAGGTGGCACGGAGAATTTTGGATTCTCAGGGATGGGTTCGATTCTCATAGTCCTAG >Homo_sapiensGlu GTTCTTGTAGTTGAAATACAACGATGGTTTTTCATATCATTGGTCGTGGTTGTAGTCCGTGCGAGAATA >Homo_sapiensGly ACTCTTTTAGTATAAATAGTACCGTTAACTTCCAATTAACTAGTTTTGACAACATTCAAAAAAGAGTA >Homo_sapiensHis GTAAATATAGTTTAACCAAAACATCAGATTGTGAATCTGACAACAGAGGCTTACGACCCCTTATTTACC >Homo_sapiensIso AGAAATATGTCTGATAAAAGAGTTACTTTGATAGAGTAAATAATAGGAGCTTAAACCCCCTTATTTCTA >Homo_sapiensLeuCun ACTTTTAAAGGATAACAGCTATCCATTGGTCTTAGGCCCCAAAAATTTTGGTGCAACTCCAAATAAAAGTA 14M2 Bioinfo Paris-Saclay 2015-2016

15 Exercise M2 Bioinfo Paris-Saclay 2015-201615 The same as previously, but with these new sequences. 1. Compute an alignment of the previous sequences, by using ClustalW or ClustalO: http://www.ebi.ac.uk/Tools/msa/clustalw2/ (do not forget to put the « DNA » option) http://www.ebi.ac.uk/Tools/msa/clustalw2/ 2. Copy/paste the result in RNAalifold : http://rna.tbi.univie.ac.at/cgi-bin/RNAalifold.cgihttp://rna.tbi.univie.ac.at/cgi-bin/RNAalifold.cgi 3. Look at the result. What happened ? Why ?

16 MAFFT alignment >Homo_sapiensArg TGGTATATAGT---TTAAACAAAACGAATGATTTCGACTCATTAAAT---TATGATAA---TCATATTTACCAA >Homo_sapiensGly ACTCTTTTAGT---ATAAATAGTACCGTTAACTTCCAATTAACTAGT---TTTGACAACATTCAAAAAAGAGTA >Homo_sapiensHis GTAAATATAGT---TTAACCAAAACATCAGATTGTGAATCTGACAAC--AGAGGCTTACGACCCCTTATTTACC >Homo_sapiensIso AGAAATATGTC---TGATAAAAGAGTTACTTTGATAGAGTAAATAAT--AGGAGCTTAAACCCCCTTATTTCTA >Homo_sapiensGlu GTTCTTGTAGT---TGAAATACAACGATGGTTTTTCATATCATTGGT--CGTGGTTGTAGTCCGTGCGAGAATA >Homo_sapiensLeuCun ACTTTTAAAGG---ATAACAGCTATCCATTGGTCTTAGGCCCCAAAAATTTTGGTGCAACTCCAAATAAAAGTA >Homo_sapiensAsn TAGATTGAAGCCAGTTGATTAGGGTGCTTAGCTGTTAACTAAGTGTT-TGTGGGTTTAAGTCCCATTGGTCTAG >Homo_sapiensGln TAGGATGGGGTGTGATAGGTGGCACGGAGAATTTTGGATTCTCAGGG--ATGGGTTCGATTCTCATAGTCCTAG >Homo_sapiensCys AGCTCCGAGGT-----GATTTTCATATTGAATTGCAAATTCGAAGAA---GCAGCTTCAAACCTGCCGGGGCTT >Homo_sapiensAsp AAGGTATTAGA---AAAACCATTTCATAACTTTGTCAAAGTTAAATT---ATAGGCTAAATCCTATATATCTTA 16M2 Bioinfo Paris-Saclay 2015-2016

17 RNAalifold 17M2 Bioinfo Paris-Saclay 2015-2016 RNAalifold finds a common but much less conserved structure.

18 Prediction by Homology Simultaneous folding and alignment 18M2 Bioinfo Paris-Saclay 2015-2016

19 Problem specification  Data : a set of sequences  Output : a sequence alignment, and a common secondary structure. 19M2 Bioinfo Paris-Saclay 2015-2016

20 Approaches  The reference approach: Sankoff’s algorithm (1985)  Algorithmic approach: dynamic programming  Complexity : n 3k for k sequences of length n  There are several implementatons, herer are two of them (with constraints):  Foldalign (Gorodkin, Heyer, Stormo 1997, Havgaard, Lyngso, Stormo, Gorodkin 2005).  Dynalign (Mathews, Turner 2002)  Heuristics based on this algorithm :  LocaRNA (http://rna.informatik.uni- freiburg.de:8080/LocARNA.jsp).http://rna.informatik.uni- freiburg.de:8080/LocARNA.jsp 20M2 Bioinfo Paris-Saclay 2015-2016

21 Exercise M2 Bioinfo Paris-Saclay 2015-201621 1. Take the two previous sets of sequences (one after the other) and run LocARNA. http://rna.informatik.uni-freiburg.de:8080/LocARNA/Input.jsp Look at the results. http://rna.informatik.uni-freiburg.de:8080/LocARNA/Input.jsp 2. Consider the first set only. Run LocARNA with the first two sequences, then the first three, and so on. How many sequences do you need to get the right tRNA structure?

22 Sankoff’s algorithm in a few words :  Data : a set of sequences  Parameters : a score matrix, giving a score S ij,kl for each alignment of pairs of nucleotides.  Output : a sequence alignment, and a common secondary structure.  Method : dynamic programming.  It is a bit complicated, so we will study a simplified version of the algorithm : Foldalign.  Two sequences only  No multiloop allowed in the secondary structure  Simplified score matrix 22M2 Bioinfo Paris-Saclay 2015-2016

23 23M2 Bioinfo Paris-Saclay 2015-2016

24 Recurrence relation for Foldalign 24M2 Bioinfo Paris-Saclay 2015-2016

25 25M2 Bioinfo Paris-Saclay 2015-2016

26 26M2 Bioinfo Paris-Saclay 2015-2016

27 27M2 Bioinfo Paris-Saclay 2015-2016

28 28M2 Bioinfo Paris-Saclay 2015-2016

29 29M2 Bioinfo Paris-Saclay 2015-2016

30 30M2 Bioinfo Paris-Saclay 2015-2016

31 From energy minimization to Boltzmann equilibrium? M2 Bioinfo Paris-Saclay 2015-201631

32 Optimization methods can be overly sensitive to fluctuations of the energy model Example:  Get RFAM seed alignment for D1-D4 domain of the Group II intron  Extract A. capsulatum ( Acidobacterium_capsu.1 ) sequence  Run RNAFold on sequence using default parameters  Rerun RNAFold using latest energy parameters Denise Ponty - Tuto ARN - IGM@Seillac'1232 Stability (Turner 2004) RNA ACGAUCGCGA CUACGUGCAU CGCGGCACGA CUGCGAUCUG CAUCGGA... Stability (Turner 1999) <ε<ε

33 Probabilistic approaches in RNA folding  RNA in silico paradigm shift:  From single structure, minimal free-energy folding…  … to ensemble approaches. …CAGUAGCCGAUCGCAGCUAGCGUA… Ensemble diversity? Structure likelihood? Evolutionary robustness? UnaFold, RNAFold, Sfold… M2 Bioinfo Paris-Saclay 2015-201633

34 Probabilistic approaches indicate uncertainty and suggest alternative conformations Example: >ENA|M10740|M10740.1 Saccharomyces cerevisiae Phe-tRNA. : Location:1..76 GCGGATTTAGCTCAGTTGGGAGAGCGCCAGACTGAAGATTTGGAGGTCCTGTGTTCGATCCACAGAATTCGCACCA M2 Bioinfo Paris-Saclay 2015-201634 Native structure RNAFold -p « dot-plot »

35 ij i+1j-1 i i+1 j j i j-1 i kk+1 j Nussinov’s algorithm (1978) 1. 2. 3. 4. Partition function algorithms can be adapted from non-ambiguous* DP scheme Is this decomposition ambiguous? * Ambiguous = Multiple ways to generate a structure 35M2 Bioinfo Paris-Saclay 2015-2016

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